Overview

Dataset statistics

Number of variables25
Number of observations96211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.5 MiB
Average record size in memory223.0 B

Variable types

Categorical9
DateTime1
Numeric15

Alerts

order_status has constant value "delivered"Constant
customer_id has a high cardinality: 96211 distinct valuesHigh cardinality
customer_unique_id has a high cardinality: 93104 distinct valuesHigh cardinality
customer_city has a high cardinality: 4083 distinct valuesHigh cardinality
product_category_name_english has a high cardinality: 72 distinct valuesHigh cardinality
order_purchase_time has a high cardinality: 602 distinct valuesHigh cardinality
payment_value is highly overall correlated with sum_price and 2 other fieldsHigh correlation
sum_price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
sum_freight_value is highly overall correlated with payment_valueHigh correlation
product_weight_g is highly overall correlated with payment_value and 4 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
recence is highly overall correlated with recence_scoreHigh correlation
recence_score is highly overall correlated with recenceHigh correlation
payment_type is highly imbalanced (57.9%)Imbalance
customer_id is uniformly distributedUniform
customer_unique_id is uniformly distributedUniform
customer_id has unique valuesUnique
length_comment_title has 85020 (88.4%) zerosZeros
length_comment_message has 57489 (59.8%) zerosZeros
product_description_lenght has 1359 (1.4%) zerosZeros
product_photos_qty has 1359 (1.4%) zerosZeros

Reproduction

Analysis started2023-02-13 12:50:00.887464
Analysis finished2023-02-13 12:50:46.454252
Duration45.57 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct96211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
9ef432eb6251297304e76186b10a928d
 
1
29a8e7dc609b301eeb68e597e333f912
 
1
d887148b2d2b9e3d51736103399c3227
 
1
8c15169cec84935673c0356c2f151da4
 
1
27e4e9e54add87b994001667ceb67802
 
1
Other values (96206)
96206 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3078752
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96211 ?
Unique (%)100.0%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row31f31efcb333fcbad2b1371c8cf0fa84
3rd rowb0830fb4747a6c6d20dea0b8c802d7ef
4th row41ce2a54c0b03bf3443c3d931a367089
5th rowf88197465ea7920adcdbec7375364d82

Common Values

ValueCountFrequency (%)
9ef432eb6251297304e76186b10a928d 1
 
< 0.1%
29a8e7dc609b301eeb68e597e333f912 1
 
< 0.1%
d887148b2d2b9e3d51736103399c3227 1
 
< 0.1%
8c15169cec84935673c0356c2f151da4 1
 
< 0.1%
27e4e9e54add87b994001667ceb67802 1
 
< 0.1%
b83c6d5f769b0e788a6bbd435c6036aa 1
 
< 0.1%
3615ad4473507f4acd0c1511578b796d 1
 
< 0.1%
27b22920b041f339fc2ee118c3597c8a 1
 
< 0.1%
f2466a19138a60af2319a3693c6b2b9e 1
 
< 0.1%
1d1ab35efaaa5ca38a3b34695e63bf03 1
 
< 0.1%
Other values (96201) 96201
> 99.9%

Length

2023-02-13T13:50:46.521755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9ef432eb6251297304e76186b10a928d 1
 
< 0.1%
7711cf624183d843aafe81855097bc37 1
 
< 0.1%
41ce2a54c0b03bf3443c3d931a367089 1
 
< 0.1%
f88197465ea7920adcdbec7375364d82 1
 
< 0.1%
8ab97904e6daea8866dbdbc4fb7aad2c 1
 
< 0.1%
503740e9ca751ccdda7ba28e9ab8f608 1
 
< 0.1%
9bdf08b4b3b52b5526ff42d37d47f222 1
 
< 0.1%
f54a9f0e6b351c431402b8461ea51999 1
 
< 0.1%
31ad1d1b63eb9962463f764d4e6e0c9d 1
 
< 0.1%
494dded5b201313c64ed7f100595b95c 1
 
< 0.1%
Other values (96201) 96201
> 99.9%

Most occurring characters

ValueCountFrequency (%)
2 192905
 
6.3%
c 192777
 
6.3%
5 192746
 
6.3%
f 192673
 
6.3%
1 192645
 
6.3%
8 192634
 
6.3%
b 192623
 
6.3%
3 192599
 
6.3%
7 192521
 
6.3%
9 192382
 
6.2%
Other values (6) 1152247
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1924091
62.5%
Lowercase Letter 1154661
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 192905
10.0%
5 192746
10.0%
1 192645
10.0%
8 192634
10.0%
3 192599
10.0%
7 192521
10.0%
9 192382
10.0%
6 192303
10.0%
0 191841
10.0%
4 191515
10.0%
Lowercase Letter
ValueCountFrequency (%)
c 192777
16.7%
f 192673
16.7%
b 192623
16.7%
e 192281
16.7%
a 192157
16.6%
d 192150
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1924091
62.5%
Latin 1154661
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 192905
10.0%
5 192746
10.0%
1 192645
10.0%
8 192634
10.0%
3 192599
10.0%
7 192521
10.0%
9 192382
10.0%
6 192303
10.0%
0 191841
10.0%
4 191515
10.0%
Latin
ValueCountFrequency (%)
c 192777
16.7%
f 192673
16.7%
b 192623
16.7%
e 192281
16.7%
a 192157
16.6%
d 192150
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3078752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 192905
 
6.3%
c 192777
 
6.3%
5 192746
 
6.3%
f 192673
 
6.3%
1 192645
 
6.3%
8 192634
 
6.3%
b 192623
 
6.3%
3 192599
 
6.3%
7 192521
 
6.3%
9 192382
 
6.2%
Other values (6) 1152247
37.4%

order_status
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
delivered
96211 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters865899
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 96211
100.0%

Length

2023-02-13T13:50:46.634557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T13:50:46.748290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
delivered 96211
100.0%

Most occurring characters

ValueCountFrequency (%)
e 288633
33.3%
d 192422
22.2%
l 96211
 
11.1%
i 96211
 
11.1%
v 96211
 
11.1%
r 96211
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 865899
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 288633
33.3%
d 192422
22.2%
l 96211
 
11.1%
i 96211
 
11.1%
v 96211
 
11.1%
r 96211
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 865899
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 288633
33.3%
d 192422
22.2%
l 96211
 
11.1%
i 96211
 
11.1%
v 96211
 
11.1%
r 96211
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 288633
33.3%
d 192422
22.2%
l 96211
 
11.1%
i 96211
 
11.1%
v 96211
 
11.1%
r 96211
 
11.1%
Distinct95689
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2017-01-05 11:56:06
Maximum2018-08-29 15:00:37
2023-02-13T13:50:46.870827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:47.049912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

review_score
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1218468
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative643
Negative (%)0.7%
Memory size1.5 MiB
2023-02-13T13:50:47.176671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q14
median5
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3472712
Coefficient of variation (CV)0.32686106
Kurtosis1.5191145
Mean4.1218468
Median Absolute Deviation (MAD)0
Skewness-1.5746366
Sum396567
Variance1.8151397
MonotonicityNot monotonic
2023-02-13T13:50:47.275481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 56606
58.8%
4 18837
 
19.6%
1 9314
 
9.7%
3 7896
 
8.2%
2 2915
 
3.0%
-1 643
 
0.7%
ValueCountFrequency (%)
-1 643
 
0.7%
1 9314
 
9.7%
2 2915
 
3.0%
3 7896
 
8.2%
4 18837
 
19.6%
5 56606
58.8%
ValueCountFrequency (%)
5 56606
58.8%
4 18837
 
19.6%
3 7896
 
8.2%
2 2915
 
3.0%
1 9314
 
9.7%
-1 643
 
0.7%

length_comment_title
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3571629
Minimum0
Maximum26
Zeros85020
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:47.402600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3093382
Coefficient of variation (CV)3.175255
Kurtosis11.900615
Mean1.3571629
Median Absolute Deviation (MAD)0
Skewness3.4823309
Sum130574
Variance18.570396
MonotonicityNot monotonic
2023-02-13T13:50:47.533125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 85020
88.4%
9 1989
 
2.1%
5 1111
 
1.2%
15 860
 
0.9%
3 696
 
0.7%
10 559
 
0.6%
17 467
 
0.5%
13 466
 
0.5%
25 404
 
0.4%
14 386
 
0.4%
Other values (17) 4253
 
4.4%
ValueCountFrequency (%)
0 85020
88.4%
1 160
 
0.2%
2 253
 
0.3%
3 696
 
0.7%
4 161
 
0.2%
5 1111
 
1.2%
6 236
 
0.2%
7 362
 
0.4%
8 325
 
0.3%
9 1989
 
2.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 404
0.4%
24 206
0.2%
23 206
0.2%
22 192
0.2%
21 226
0.2%
20 335
0.3%
19 255
0.3%
18 291
0.3%
17 467
0.5%

length_comment_message
Real number (ℝ)

Distinct209
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.006195
Minimum0
Maximum208
Zeros57489
Zeros (%)59.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:47.678585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q339
95-th percentile141
Maximum208
Range208
Interquartile range (IQR)39

Descriptive statistics

Standard deviation47.125174
Coefficient of variation (CV)1.7449764
Kurtosis3.6781595
Mean27.006195
Median Absolute Deviation (MAD)0
Skewness2.0530015
Sum2598293
Variance2220.782
MonotonicityNot monotonic
2023-02-13T13:50:47.827844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57489
59.8%
9 991
 
1.0%
5 550
 
0.6%
200 531
 
0.6%
3 510
 
0.5%
26 478
 
0.5%
10 456
 
0.5%
34 450
 
0.5%
20 438
 
0.5%
31 433
 
0.5%
Other values (199) 33885
35.2%
ValueCountFrequency (%)
0 57489
59.8%
1 96
 
0.1%
2 195
 
0.2%
3 510
 
0.5%
4 97
 
0.1%
5 550
 
0.6%
6 205
 
0.2%
7 227
 
0.2%
8 236
 
0.2%
9 991
 
1.0%
ValueCountFrequency (%)
208 1
 
< 0.1%
207 1
 
< 0.1%
206 1
 
< 0.1%
205 1
 
< 0.1%
204 12
 
< 0.1%
203 14
 
< 0.1%
202 10
 
< 0.1%
201 19
 
< 0.1%
200 531
0.6%
199 300
0.3%

payment_type
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
credit_card
71918 
boleto
19140 
credit_card,voucher
 
2176
voucher
 
1494
debit_card
 
1482

Length

Max length22
Median length11
Mean length10.108844
Min length6

Characters and Unicode

Total characters972582
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcredit_card,voucher
2nd rowcredit_card
3rd rowboleto
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 71918
74.8%
boleto 19140
 
19.9%
credit_card,voucher 2176
 
2.3%
voucher 1494
 
1.6%
debit_card 1482
 
1.5%
credit_card,debit_card 1
 
< 0.1%

Length

2023-02-13T13:50:48.379971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T13:50:48.535556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 71918
74.8%
boleto 19140
 
19.9%
credit_card,voucher 2176
 
2.3%
voucher 1494
 
1.6%
debit_card 1482
 
1.5%
credit_card,debit_card 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 153343
15.8%
r 153343
15.8%
d 151156
15.5%
e 98388
10.1%
t 94718
9.7%
i 75578
7.8%
_ 75578
7.8%
a 75578
7.8%
o 41950
 
4.3%
b 20623
 
2.1%
Other values (5) 32327
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 894827
92.0%
Connector Punctuation 75578
 
7.8%
Other Punctuation 2177
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 153343
17.1%
r 153343
17.1%
d 151156
16.9%
e 98388
11.0%
t 94718
10.6%
i 75578
8.4%
a 75578
8.4%
o 41950
 
4.7%
b 20623
 
2.3%
l 19140
 
2.1%
Other values (3) 11010
 
1.2%
Connector Punctuation
ValueCountFrequency (%)
_ 75578
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 894827
92.0%
Common 77755
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 153343
17.1%
r 153343
17.1%
d 151156
16.9%
e 98388
11.0%
t 94718
10.6%
i 75578
8.4%
a 75578
8.4%
o 41950
 
4.7%
b 20623
 
2.3%
l 19140
 
2.1%
Other values (3) 11010
 
1.2%
Common
ValueCountFrequency (%)
_ 75578
97.2%
, 2177
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 153343
15.8%
r 153343
15.8%
d 151156
15.5%
e 98388
10.1%
t 94718
9.7%
i 75578
7.8%
_ 75578
7.8%
a 75578
7.8%
o 41950
 
4.3%
b 20623
 
2.1%
Other values (5) 32327
 
3.3%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.926048
Minimum0
Maximum24
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:48.658880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7113286
Coefficient of variation (CV)0.92661796
Kurtosis2.3968787
Mean2.926048
Median Absolute Deviation (MAD)1
Skewness1.6057481
Sum281518
Variance7.3513027
MonotonicityNot monotonic
2023-02-13T13:50:48.787884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 46709
48.5%
2 12001
 
12.5%
3 10099
 
10.5%
4 6842
 
7.1%
10 5103
 
5.3%
5 5067
 
5.3%
8 4117
 
4.3%
6 3777
 
3.9%
7 1550
 
1.6%
9 615
 
0.6%
Other values (14) 331
 
0.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 46709
48.5%
2 12001
 
12.5%
3 10099
 
10.5%
4 6842
 
7.1%
5 5067
 
5.3%
6 3777
 
3.9%
7 1550
 
1.6%
8 4117
 
4.3%
9 615
 
0.6%
ValueCountFrequency (%)
24 18
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 3
 
< 0.1%
20 16
 
< 0.1%
18 27
 
< 0.1%
17 7
 
< 0.1%
16 5
 
< 0.1%
15 72
0.1%
14 14
 
< 0.1%

payment_value
Real number (ℝ)

Distinct27383
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.81411
Minimum9.59
Maximum13664.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:48.940885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.59
5-th percentile32.38
Q161.88
median105.28
Q3176.26
95-th percentile445.645
Maximum13664.08
Range13654.49
Interquartile range (IQR)114.38

Descriptive statistics

Standard deviation218.88163
Coefficient of variation (CV)1.3696014
Kurtosis249.57272
Mean159.81411
Median Absolute Deviation (MAD)51.52
Skewness9.3788853
Sum15375875
Variance47909.17
MonotonicityNot monotonic
2023-02-13T13:50:49.094310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.57 250
 
0.3%
35 164
 
0.2%
73.34 161
 
0.2%
116.94 131
 
0.1%
56.78 118
 
0.1%
107.78 118
 
0.1%
65 112
 
0.1%
86.15 106
 
0.1%
99.9 105
 
0.1%
67.5 104
 
0.1%
Other values (27373) 94842
98.6%
ValueCountFrequency (%)
9.59 1
< 0.1%
10.07 1
< 0.1%
10.89 1
< 0.1%
11.56 1
< 0.1%
11.62 1
< 0.1%
11.63 2
< 0.1%
12.28 1
< 0.1%
12.39 1
< 0.1%
12.89 2
< 0.1%
13.17 1
< 0.1%
ValueCountFrequency (%)
13664.08 1
< 0.1%
7274.88 1
< 0.1%
6929.31 1
< 0.1%
6922.21 1
< 0.1%
6726.66 1
< 0.1%
6081.54 1
< 0.1%
4950.34 1
< 0.1%
4764.34 1
< 0.1%
4681.78 1
< 0.1%
4513.32 1
< 0.1%

nb_items
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1420732
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:49.252421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.53844026
Coefficient of variation (CV)0.47145864
Kurtosis116.76065
Mean1.1420732
Median Absolute Deviation (MAD)0
Skewness7.5698165
Sum109880
Variance0.28991791
MonotonicityNot monotonic
2023-02-13T13:50:49.368962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 86606
90.0%
2 7372
 
7.7%
3 1301
 
1.4%
4 493
 
0.5%
5 192
 
0.2%
6 189
 
0.2%
7 22
 
< 0.1%
10 8
 
< 0.1%
8 8
 
< 0.1%
12 5
 
< 0.1%
Other values (7) 15
 
< 0.1%
ValueCountFrequency (%)
1 86606
90.0%
2 7372
 
7.7%
3 1301
 
1.4%
4 493
 
0.5%
5 192
 
0.2%
6 189
 
0.2%
7 22
 
< 0.1%
8 8
 
< 0.1%
9 3
 
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
< 0.1%
11 4
< 0.1%
10 8
< 0.1%
9 3
 
< 0.1%
8 8
< 0.1%

sum_price
Real number (ℝ)

Distinct7626
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.00125
Minimum0.85
Maximum13440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:49.517038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile19
Q145.9
median86.5
Q3149.9
95-th percentile399
Maximum13440
Range13439.15
Interquartile range (IQR)104

Descriptive statistics

Standard deviation209.11337
Coefficient of variation (CV)1.5263611
Kurtosis277.40578
Mean137.00125
Median Absolute Deviation (MAD)47.5
Skewness9.8980267
Sum13181027
Variance43728.402
MonotonicityNot monotonic
2023-02-13T13:50:49.690683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 1678
 
1.7%
69.9 1569
 
1.6%
49.9 1388
 
1.4%
89.9 1214
 
1.3%
99.9 1160
 
1.2%
79.9 982
 
1.0%
39.9 952
 
1.0%
29.9 943
 
1.0%
19.9 900
 
0.9%
29.99 856
 
0.9%
Other values (7616) 84569
87.9%
ValueCountFrequency (%)
0.85 2
< 0.1%
2.2 1
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
< 0.1%
3.49 1
 
< 0.1%
3.5 1
 
< 0.1%
3.54 1
 
< 0.1%
3.85 3
< 0.1%
ValueCountFrequency (%)
13440 1
< 0.1%
7160 1
< 0.1%
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
5934.6 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4400 1
< 0.1%

sum_freight_value
Real number (ℝ)

Distinct7864
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.784223
Minimum0
Maximum1794.96
Zeros336
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:49.859716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.87
Q113.85
median17.17
Q324.01
95-th percentile54.765
Maximum1794.96
Range1794.96
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation21.56532
Coefficient of variation (CV)0.94650232
Kurtosis586.93446
Mean22.784223
Median Absolute Deviation (MAD)4.38
Skewness12.29266
Sum2192092.9
Variance465.06303
MonotonicityNot monotonic
2023-02-13T13:50:50.013215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 2897
 
3.0%
7.78 1802
 
1.9%
14.1 1488
 
1.5%
11.85 1423
 
1.5%
18.23 1200
 
1.2%
7.39 1125
 
1.2%
15.23 809
 
0.8%
16.11 780
 
0.8%
8.72 738
 
0.8%
16.79 686
 
0.7%
Other values (7854) 83263
86.5%
ValueCountFrequency (%)
0 336
0.3%
5.7 1
 
< 0.1%
5.82 1
 
< 0.1%
5.88 2
 
< 0.1%
6.52 1
 
< 0.1%
6.53 2
 
< 0.1%
6.56 1
 
< 0.1%
6.57 5
 
< 0.1%
6.78 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1794.96 1
< 0.1%
1002.29 1
< 0.1%
711.33 1
< 0.1%
626.64 1
< 0.1%
502.98 1
< 0.1%
497.42 1
< 0.1%
497.08 1
< 0.1%
479.28 1
< 0.1%
458.73 1
< 0.1%
456.47 1
< 0.1%

customer_unique_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct93104
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
8d50f5eadf50201ccdcedfb9e2ac8455
 
15
3e43e6105506432c953e165fb2acf44c
 
9
ca77025e7201e3b30c44b472ff346268
 
7
6469f99c1f9dfae7733b25662e7f1782
 
7
1b6c7548a2a1f9037c1fd3ddfed95f33
 
7
Other values (93099)
96166 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3078752
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90315 ?
Unique (%)93.9%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd rowaf07308b275d755c9edb36a90c618231
4th row3a653a41f6f9fc3d2a113cf8398680e8
5th row7c142cf63193a1473d2e66489a9ae977

Common Values

ValueCountFrequency (%)
8d50f5eadf50201ccdcedfb9e2ac8455 15
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 9
 
< 0.1%
ca77025e7201e3b30c44b472ff346268 7
 
< 0.1%
6469f99c1f9dfae7733b25662e7f1782 7
 
< 0.1%
1b6c7548a2a1f9037c1fd3ddfed95f33 7
 
< 0.1%
63cfc61cee11cbe306bff5857d00bfe4 6
 
< 0.1%
12f5d6e1cbf93dafd9dcc19095df0b3d 6
 
< 0.1%
dc813062e0fc23409cd255f7f53c7074 6
 
< 0.1%
47c1a3033b8b77b3ab6e109eb4d5fdf3 6
 
< 0.1%
f0e310a6839dce9de1638e0fe5ab282a 6
 
< 0.1%
Other values (93094) 96136
99.9%

Length

2023-02-13T13:50:50.157501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8d50f5eadf50201ccdcedfb9e2ac8455 15
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 9
 
< 0.1%
ca77025e7201e3b30c44b472ff346268 7
 
< 0.1%
6469f99c1f9dfae7733b25662e7f1782 7
 
< 0.1%
1b6c7548a2a1f9037c1fd3ddfed95f33 7
 
< 0.1%
63cfc61cee11cbe306bff5857d00bfe4 6
 
< 0.1%
12f5d6e1cbf93dafd9dcc19095df0b3d 6
 
< 0.1%
dc813062e0fc23409cd255f7f53c7074 6
 
< 0.1%
47c1a3033b8b77b3ab6e109eb4d5fdf3 6
 
< 0.1%
f0e310a6839dce9de1638e0fe5ab282a 6
 
< 0.1%
Other values (93094) 96136
99.9%

Most occurring characters

ValueCountFrequency (%)
6 192956
 
6.3%
8 192846
 
6.3%
1 192751
 
6.3%
5 192682
 
6.3%
d 192623
 
6.3%
a 192599
 
6.3%
e 192570
 
6.3%
0 192538
 
6.3%
9 192509
 
6.3%
2 192459
 
6.3%
Other values (6) 1152219
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1924536
62.5%
Lowercase Letter 1154216
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 192956
10.0%
8 192846
10.0%
1 192751
10.0%
5 192682
10.0%
0 192538
10.0%
9 192509
10.0%
2 192459
10.0%
3 192054
10.0%
4 191952
10.0%
7 191789
10.0%
Lowercase Letter
ValueCountFrequency (%)
d 192623
16.7%
a 192599
16.7%
e 192570
16.7%
b 192444
16.7%
f 192228
16.7%
c 191752
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1924536
62.5%
Latin 1154216
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 192956
10.0%
8 192846
10.0%
1 192751
10.0%
5 192682
10.0%
0 192538
10.0%
9 192509
10.0%
2 192459
10.0%
3 192054
10.0%
4 191952
10.0%
7 191789
10.0%
Latin
ValueCountFrequency (%)
d 192623
16.7%
a 192599
16.7%
e 192570
16.7%
b 192444
16.7%
f 192228
16.7%
c 191752
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3078752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 192956
 
6.3%
8 192846
 
6.3%
1 192751
 
6.3%
5 192682
 
6.3%
d 192623
 
6.3%
a 192599
 
6.3%
e 192570
 
6.3%
0 192538
 
6.3%
9 192509
 
6.3%
2 192459
 
6.3%
Other values (6) 1152219
37.4%

customer_city
Categorical

Distinct4083
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
sao paulo
15014 
rio de janeiro
 
6574
belo horizonte
 
2687
brasilia
 
2065
curitiba
 
1483
Other values (4078)
68388 

Length

Max length32
Median length27
Mean length10.342622
Min length3

Characters and Unicode

Total characters995074
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1140 ?
Unique (%)1.2%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowbarreiras
4th rowvianopolis
5th rowsao goncalo do amarante

Common Values

ValueCountFrequency (%)
sao paulo 15014
 
15.6%
rio de janeiro 6574
 
6.8%
belo horizonte 2687
 
2.8%
brasilia 2065
 
2.1%
curitiba 1483
 
1.5%
campinas 1400
 
1.5%
porto alegre 1340
 
1.4%
salvador 1188
 
1.2%
guarulhos 1143
 
1.2%
sao bernardo do campo 908
 
0.9%
Other values (4073) 62409
64.9%

Length

2023-02-13T13:50:50.312482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao 20326
 
12.1%
paulo 15079
 
8.9%
de 9280
 
5.5%
rio 7935
 
4.7%
janeiro 6574
 
3.9%
do 4159
 
2.5%
belo 2746
 
1.6%
horizonte 2711
 
1.6%
brasilia 2074
 
1.2%
porto 1598
 
0.9%
Other values (3262) 96056
57.0%

Most occurring characters

ValueCountFrequency (%)
a 164130
16.5%
o 122347
12.3%
i 76177
 
7.7%
r 73931
 
7.4%
72327
 
7.3%
e 64744
 
6.5%
s 60924
 
6.1%
n 44229
 
4.4%
u 43465
 
4.4%
l 43382
 
4.4%
Other values (21) 229418
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 922296
92.7%
Space Separator 72327
 
7.3%
Dash Punctuation 227
 
< 0.1%
Other Punctuation 222
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 164130
17.8%
o 122347
13.3%
i 76177
 
8.3%
r 73931
 
8.0%
e 64744
 
7.0%
s 60924
 
6.6%
n 44229
 
4.8%
u 43465
 
4.7%
l 43382
 
4.7%
p 35983
 
3.9%
Other values (16) 192984
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
72327
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 227
100.0%
Other Punctuation
ValueCountFrequency (%)
' 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 922296
92.7%
Common 72778
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 164130
17.8%
o 122347
13.3%
i 76177
 
8.3%
r 73931
 
8.0%
e 64744
 
7.0%
s 60924
 
6.6%
n 44229
 
4.8%
u 43465
 
4.7%
l 43382
 
4.7%
p 35983
 
3.9%
Other values (16) 192984
20.9%
Common
ValueCountFrequency (%)
72327
99.4%
- 227
 
0.3%
' 222
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 995074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 164130
16.5%
o 122347
12.3%
i 76177
 
7.7%
r 73931
 
7.4%
72327
 
7.3%
e 64744
 
6.5%
s 60924
 
6.1%
n 44229
 
4.4%
u 43465
 
4.4%
l 43382
 
4.4%
Other values (21) 229418
23.1%

customer_state
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
SP
40406 
RJ
12310 
MG
11319 
RS
5328 
PR
4903 
Other values (22)
21945 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters192422
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowBA
4th rowGO
5th rowRN

Common Values

ValueCountFrequency (%)
SP 40406
42.0%
RJ 12310
 
12.8%
MG 11319
 
11.8%
RS 5328
 
5.5%
PR 4903
 
5.1%
SC 3537
 
3.7%
BA 3253
 
3.4%
DF 2074
 
2.2%
ES 1992
 
2.1%
GO 1950
 
2.0%
Other values (17) 9139
 
9.5%

Length

2023-02-13T13:50:50.448730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 40406
42.0%
rj 12310
 
12.8%
mg 11319
 
11.8%
rs 5328
 
5.5%
pr 4903
 
5.1%
sc 3537
 
3.7%
ba 3253
 
3.4%
df 2074
 
2.2%
es 1992
 
2.1%
go 1950
 
2.0%
Other values (17) 9139
 
9.5%

Most occurring characters

ValueCountFrequency (%)
S 52296
27.2%
P 48896
25.4%
R 23334
12.1%
M 13763
 
7.2%
G 13269
 
6.9%
J 12310
 
6.4%
A 5596
 
2.9%
E 5184
 
2.7%
C 4890
 
2.5%
B 3769
 
2.0%
Other values (7) 9115
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 192422
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 52296
27.2%
P 48896
25.4%
R 23334
12.1%
M 13763
 
7.2%
G 13269
 
6.9%
J 12310
 
6.4%
A 5596
 
2.9%
E 5184
 
2.7%
C 4890
 
2.5%
B 3769
 
2.0%
Other values (7) 9115
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 192422
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 52296
27.2%
P 48896
25.4%
R 23334
12.1%
M 13763
 
7.2%
G 13269
 
6.9%
J 12310
 
6.4%
A 5596
 
2.9%
E 5184
 
2.7%
C 4890
 
2.5%
B 3769
 
2.0%
Other values (7) 9115
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 52296
27.2%
P 48896
25.4%
R 23334
12.1%
M 13763
 
7.2%
G 13269
 
6.9%
J 12310
 
6.4%
A 5596
 
2.9%
E 5184
 
2.7%
C 4890
 
2.5%
B 3769
 
2.0%
Other values (7) 9115
 
4.7%
Distinct2936
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean782.03096
Minimum0
Maximum3992
Zeros1359
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:50.583447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138
Q1341
median600
Q3986
95-th percentile2118
Maximum3992
Range3992
Interquartile range (IQR)645

Descriptive statistics

Standard deviation655.73156
Coefficient of variation (CV)0.8384982
Kurtosis4.8019174
Mean782.03096
Median Absolute Deviation (MAD)300
Skewness1.9728338
Sum75239981
Variance429983.87
MonotonicityNot monotonic
2023-02-13T13:50:50.739802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1359
 
1.4%
1893 570
 
0.6%
341 530
 
0.6%
492 523
 
0.5%
903 471
 
0.5%
245 464
 
0.5%
348 456
 
0.5%
236 419
 
0.4%
366 392
 
0.4%
575 353
 
0.4%
Other values (2926) 90674
94.2%
ValueCountFrequency (%)
0 1359
1.4%
4 6
 
< 0.1%
8 1
 
< 0.1%
15 1
 
< 0.1%
20 6
 
< 0.1%
26 2
 
< 0.1%
27 3
 
< 0.1%
28 2
 
< 0.1%
30 6
 
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 1
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 2
 
< 0.1%
3963 1
 
< 0.1%
3956 1
 
< 0.1%
3954 2
 
< 0.1%
3950 1
 
< 0.1%
3948 1
 
< 0.1%
3947 6
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2185509
Minimum0
Maximum20
Zeros1359
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:50.887435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7529091
Coefficient of variation (CV)0.79011444
Kurtosis4.3790034
Mean2.2185509
Median Absolute Deviation (MAD)1
Skewness1.819054
Sum213449
Variance3.0726903
MonotonicityNot monotonic
2023-02-13T13:50:51.006543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 46882
48.7%
2 18649
 
19.4%
3 10947
 
11.4%
4 7392
 
7.7%
5 4874
 
5.1%
6 3319
 
3.4%
7 1371
 
1.4%
0 1359
 
1.4%
8 665
 
0.7%
10 314
 
0.3%
Other values (10) 439
 
0.5%
ValueCountFrequency (%)
0 1359
 
1.4%
1 46882
48.7%
2 18649
 
19.4%
3 10947
 
11.4%
4 7392
 
7.7%
5 4874
 
5.1%
6 3319
 
3.4%
7 1371
 
1.4%
8 665
 
0.7%
9 280
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 4
 
< 0.1%
17 8
 
< 0.1%
15 11
 
< 0.1%
14 6
 
< 0.1%
13 26
 
< 0.1%
12 43
 
< 0.1%
11 59
 
0.1%
10 314
0.3%

product_weight_g
Real number (ℝ)

Distinct2152
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2095.437
Minimum-1
Maximum40425
Zeros6
Zeros (%)< 0.1%
Negative16
Negative (%)< 0.1%
Memory size1.5 MiB
2023-02-13T13:50:51.188545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile125
Q1300
median700
Q31800
95-th percentile9750
Maximum40425
Range40426
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3750.9211
Coefficient of variation (CV)1.7900424
Kurtosis16.413126
Mean2095.437
Median Absolute Deviation (MAD)500
Skewness3.6095692
Sum2.0160409 × 108
Variance14069409
MonotonicityNot monotonic
2023-02-13T13:50:51.360783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 5795
 
6.0%
150 4557
 
4.7%
250 3927
 
4.1%
300 3627
 
3.8%
400 3104
 
3.2%
100 3042
 
3.2%
350 2778
 
2.9%
500 2285
 
2.4%
600 2226
 
2.3%
700 1703
 
1.8%
Other values (2142) 63167
65.7%
ValueCountFrequency (%)
-1 16
 
< 0.1%
0 6
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 785
0.8%
53 2
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
58 1
 
< 0.1%
60 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 243
0.3%
29800 1
 
< 0.1%
29700 2
 
< 0.1%
29600 5
 
< 0.1%
29500 1
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%
29050 4
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.075896
Minimum-1
Maximum105
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)< 0.1%
Memory size1.5 MiB
2023-02-13T13:50:51.541815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile16
Q118
median25
Q338
95-th percentile61
Maximum105
Range106
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.096074
Coefficient of variation (CV)0.53518188
Kurtosis3.7716934
Mean30.075896
Median Absolute Deviation (MAD)8
Skewness1.7650211
Sum2893632
Variance259.08361
MonotonicityNot monotonic
2023-02-13T13:50:51.716462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 14918
 
15.5%
20 8897
 
9.2%
30 6181
 
6.4%
17 5219
 
5.4%
18 5011
 
5.2%
19 4046
 
4.2%
25 4042
 
4.2%
40 3463
 
3.6%
22 3348
 
3.5%
35 2514
 
2.6%
Other values (90) 38572
40.1%
ValueCountFrequency (%)
-1 16
 
< 0.1%
7 30
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 7
 
< 0.1%
11 82
0.1%
12 34
 
< 0.1%
13 47
 
< 0.1%
14 117
0.1%
15 175
0.2%
ValueCountFrequency (%)
105 281
0.3%
104 29
 
< 0.1%
103 34
 
< 0.1%
102 42
 
< 0.1%
101 87
 
0.1%
100 297
0.3%
99 31
 
< 0.1%
98 41
 
< 0.1%
97 10
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.42888
Minimum-1
Maximum105
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)< 0.1%
Memory size1.5 MiB
2023-02-13T13:50:51.879512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile3
Q18
median13
Q320
95-th percentile44
Maximum105
Range106
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.26808
Coefficient of variation (CV)0.80760709
Kurtosis7.4824695
Mean16.42888
Median Absolute Deviation (MAD)6
Skewness2.2585977
Sum1580639
Variance176.04195
MonotonicityNot monotonic
2023-02-13T13:50:52.055142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 8323
 
8.7%
20 5693
 
5.9%
12 5533
 
5.8%
15 5527
 
5.7%
11 5349
 
5.6%
2 4336
 
4.5%
4 4136
 
4.3%
8 3964
 
4.1%
16 3881
 
4.0%
5 3823
 
4.0%
Other values (93) 45646
47.4%
ValueCountFrequency (%)
-1 16
 
< 0.1%
2 4336
4.5%
3 2297
 
2.4%
4 4136
4.3%
5 3823
4.0%
6 2966
 
3.1%
7 3642
3.8%
8 3964
4.1%
9 2755
 
2.9%
10 8323
8.7%
ValueCountFrequency (%)
105 105
0.1%
104 10
 
< 0.1%
103 37
 
< 0.1%
102 7
 
< 0.1%
100 36
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 1
 
< 0.1%
96 5
 
< 0.1%
95 21
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.002661
Minimum-1
Maximum118
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)< 0.1%
Memory size1.5 MiB
2023-02-13T13:50:52.218474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range119
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.730453
Coefficient of variation (CV)0.5099607
Kurtosis4.5873749
Mean23.002661
Median Absolute Deviation (MAD)6
Skewness1.7135749
Sum2213109
Variance137.60353
MonotonicityNot monotonic
2023-02-13T13:50:52.379817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 10209
 
10.6%
11 8974
 
9.3%
15 7715
 
8.0%
16 7188
 
7.5%
30 6297
 
6.5%
12 4740
 
4.9%
13 4592
 
4.8%
14 3981
 
4.1%
18 3481
 
3.6%
40 3296
 
3.4%
Other values (84) 35738
37.1%
ValueCountFrequency (%)
-1 16
 
< 0.1%
6 2
 
< 0.1%
7 5
 
< 0.1%
8 16
 
< 0.1%
9 46
 
< 0.1%
10 66
 
0.1%
11 8974
9.3%
12 4740
4.9%
13 4592
4.8%
14 3981
4.1%
ValueCountFrequency (%)
118 7
 
< 0.1%
105 13
 
< 0.1%
104 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 40
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
93 12
 
< 0.1%
Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
bed_bath_table
9153 
health_beauty
8578 
sports_leisure
7474 
computers_accessories
6489 
furniture_decor
6169 
Other values (67)
58348 

Length

Max length39
Median length31
Mean length12.773207
Min length3

Characters and Unicode

Total characters1228923
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowbaby
3rd rowperfumery
4th rowauto
5th rowpet_shop

Common Values

ValueCountFrequency (%)
bed_bath_table 9153
 
9.5%
health_beauty 8578
 
8.9%
sports_leisure 7474
 
7.8%
computers_accessories 6489
 
6.7%
furniture_decor 6169
 
6.4%
housewares 5670
 
5.9%
watches_gifts 5474
 
5.7%
telephony 4076
 
4.2%
auto 3783
 
3.9%
toys 3747
 
3.9%
Other values (62) 35598
37.0%

Length

2023-02-13T13:50:52.559172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bed_bath_table 9153
 
9.5%
health_beauty 8578
 
8.9%
sports_leisure 7474
 
7.8%
computers_accessories 6489
 
6.7%
furniture_decor 6169
 
6.4%
housewares 5670
 
5.9%
watches_gifts 5474
 
5.7%
telephony 4076
 
4.2%
auto 3783
 
3.9%
toys 3747
 
3.9%
Other values (62) 35598
37.0%

Most occurring characters

ValueCountFrequency (%)
e 148905
12.1%
s 116596
 
9.5%
t 108382
 
8.8%
o 91321
 
7.4%
a 83611
 
6.8%
r 82212
 
6.7%
_ 81487
 
6.6%
u 63059
 
5.1%
c 58345
 
4.7%
i 50672
 
4.1%
Other values (15) 344333
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1147187
93.3%
Connector Punctuation 81487
 
6.6%
Decimal Number 249
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 148905
13.0%
s 116596
 
10.2%
t 108382
 
9.4%
o 91321
 
8.0%
a 83611
 
7.3%
r 82212
 
7.2%
u 63059
 
5.5%
c 58345
 
5.1%
i 50672
 
4.4%
h 49140
 
4.3%
Other values (13) 294944
25.7%
Connector Punctuation
ValueCountFrequency (%)
_ 81487
100.0%
Decimal Number
ValueCountFrequency (%)
2 249
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1147187
93.3%
Common 81736
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 148905
13.0%
s 116596
 
10.2%
t 108382
 
9.4%
o 91321
 
8.0%
a 83611
 
7.3%
r 82212
 
7.2%
u 63059
 
5.5%
c 58345
 
5.1%
i 50672
 
4.4%
h 49140
 
4.3%
Other values (13) 294944
25.7%
Common
ValueCountFrequency (%)
_ 81487
99.7%
2 249
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1228923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 148905
12.1%
s 116596
 
9.5%
t 108382
 
8.8%
o 91321
 
7.4%
a 83611
 
6.8%
r 82212
 
6.7%
_ 81487
 
6.6%
u 63059
 
5.1%
c 58345
 
4.7%
i 50672
 
4.1%
Other values (15) 344333
28.0%
Distinct602
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
11/24/2017
 
1147
11/25/2017
 
487
11/27/2017
 
395
11/26/2017
 
382
11/28/2017
 
372
Other values (597)
93428 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters962110
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10/02/2017
2nd row09/04/2017
3rd row07/24/2018
4th row08/08/2018
5th row11/18/2017

Common Values

ValueCountFrequency (%)
11/24/2017 1147
 
1.2%
11/25/2017 487
 
0.5%
11/27/2017 395
 
0.4%
11/26/2017 382
 
0.4%
11/28/2017 372
 
0.4%
05/07/2018 363
 
0.4%
08/06/2018 363
 
0.4%
05/14/2018 355
 
0.4%
08/07/2018 353
 
0.4%
05/16/2018 351
 
0.4%
Other values (592) 91643
95.3%

Length

2023-02-13T13:50:52.713960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/24/2017 1147
 
1.2%
11/25/2017 487
 
0.5%
11/27/2017 395
 
0.4%
11/26/2017 382
 
0.4%
11/28/2017 372
 
0.4%
05/07/2018 363
 
0.4%
08/06/2018 363
 
0.4%
05/14/2018 355
 
0.4%
08/07/2018 353
 
0.4%
05/16/2018 351
 
0.4%
Other values (592) 91643
95.3%

Most occurring characters

ValueCountFrequency (%)
0 217045
22.6%
/ 192422
20.0%
1 171900
17.9%
2 150181
15.6%
8 72795
 
7.6%
7 62839
 
6.5%
3 23000
 
2.4%
5 20166
 
2.1%
4 19492
 
2.0%
6 19216
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 769688
80.0%
Other Punctuation 192422
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217045
28.2%
1 171900
22.3%
2 150181
19.5%
8 72795
 
9.5%
7 62839
 
8.2%
3 23000
 
3.0%
5 20166
 
2.6%
4 19492
 
2.5%
6 19216
 
2.5%
9 13054
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 192422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 962110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 217045
22.6%
/ 192422
20.0%
1 171900
17.9%
2 150181
15.6%
8 72795
 
7.6%
7 62839
 
6.5%
3 23000
 
2.4%
5 20166
 
2.1%
4 19492
 
2.0%
6 19216
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 962110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 217045
22.6%
/ 192422
20.0%
1 171900
17.9%
2 150181
15.6%
8 72795
 
7.6%
7 62839
 
6.5%
3 23000
 
2.4%
5 20166
 
2.1%
4 19492
 
2.0%
6 19216
 
2.0%

recence
Real number (ℝ)

Distinct602
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.27314
Minimum54
Maximum655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-13T13:50:52.848166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile77
Q1169
median276
Q3401
95-th percentile570
Maximum655
Range601
Interquartile range (IQR)232

Descriptive statistics

Standard deviation150.90353
Coefficient of variation (CV)0.51630995
Kurtosis-0.78139219
Mean292.27314
Median Absolute Deviation (MAD)113
Skewness0.38583269
Sum28119891
Variance22771.876
MonotonicityNot monotonic
2023-02-13T13:50:53.004357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
332 1167
 
1.2%
331 501
 
0.5%
329 411
 
0.4%
330 392
 
0.4%
328 388
 
0.4%
77 365
 
0.4%
168 362
 
0.4%
76 356
 
0.4%
161 351
 
0.4%
159 351
 
0.4%
Other values (592) 91567
95.2%
ValueCountFrequency (%)
54 11
 
< 0.1%
55 43
 
< 0.1%
56 70
 
0.1%
57 75
 
0.1%
58 71
 
0.1%
59 99
 
0.1%
60 146
0.2%
61 185
0.2%
62 245
0.3%
63 257
0.3%
ValueCountFrequency (%)
655 32
< 0.1%
654 4
 
< 0.1%
653 4
 
< 0.1%
652 4
 
< 0.1%
651 5
 
< 0.1%
650 6
 
< 0.1%
649 9
 
< 0.1%
648 12
 
< 0.1%
647 10
 
< 0.1%
646 16
< 0.1%

recence_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size845.8 KiB
3
19431 
5
19325 
4
19231 
1
19179 
2
19045 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96211
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row5
4th row5
5th row2

Common Values

ValueCountFrequency (%)
3 19431
20.2%
5 19325
20.1%
4 19231
20.0%
1 19179
19.9%
2 19045
19.8%

Length

2023-02-13T13:50:53.151311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T13:50:53.283091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 19431
20.2%
5 19325
20.1%
4 19231
20.0%
1 19179
19.9%
2 19045
19.8%

Most occurring characters

ValueCountFrequency (%)
3 19431
20.2%
5 19325
20.1%
4 19231
20.0%
1 19179
19.9%
2 19045
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96211
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 19431
20.2%
5 19325
20.1%
4 19231
20.0%
1 19179
19.9%
2 19045
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 96211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 19431
20.2%
5 19325
20.1%
4 19231
20.0%
1 19179
19.9%
2 19045
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 19431
20.2%
5 19325
20.1%
4 19231
20.0%
1 19179
19.9%
2 19045
19.8%

Interactions

2023-02-13T13:50:42.716452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:08.460078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:10.735664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:13.334578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.020140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:18.290962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.675433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.339403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:25.691872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:28.104117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:30.637050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:33.143649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:35.860475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:38.060068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.462080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:42.876814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:08.612532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:10.887023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:13.501530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.164415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:18.510881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.821721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.489649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:25.838345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:28.253150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:30.789710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:33.302731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.005461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:38.205857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.610205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.014406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:08.755835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.017328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:13.655910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.293593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:18.656046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.956244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.627570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:25.979665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:28.393373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:30.933099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:33.448429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.140692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:38.362226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.755555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.146235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:08.892628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.148528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:13.885849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.439471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:18.793279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:21.090549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.767420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:26.114143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:28.537466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:31.077193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:33.589448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.272737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:38.600353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.885609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.287268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.028707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.292993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:14.123051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.566808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:18.958667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:21.227576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.906275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:26.256261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:28.684711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:31.243743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:33.731537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.407456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:38.848536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.025166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.430307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.177322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.441183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:14.283982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.711753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:19.164299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:21.375954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.050724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:26.407313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:28.865654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:31.401554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:33.889000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.555831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.015253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.193701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.578740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.328405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.596797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:14.460945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:16.859308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:19.336175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:21.546954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.204230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:26.565768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:29.080450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:31.571320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:34.388127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.715660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.160513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.372707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.727188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.480335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.743665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:14.708187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.015163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:19.479366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:21.722263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.354329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:26.721723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:29.264930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:31.747097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:34.565653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:36.871994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.315661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.516919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:43.896164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.626956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:11.890911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:14.894790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.159027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:19.638229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:21.872181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.510258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:26.892810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:29.439381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:31.924562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:34.740916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.019427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.459192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.663306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:44.054007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.785909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:12.043040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:15.044499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.325207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:19.791413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:22.038403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.672963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:27.064282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:29.596682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:32.123114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:34.900514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.172598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.612682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.812569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:44.213567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:09.940211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:12.236781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:15.193399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.476608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:19.944320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:22.192958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.825388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:27.239452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:29.759707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:32.390498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:35.083758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.348444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.765455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:41.972690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:44.374260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:10.096235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:12.487474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:15.360335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.645899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.109173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:22.374462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:24.984248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:27.486238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:29.922244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:32.554985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:35.255638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.527307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:39.911858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:42.152360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:44.541426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:10.243300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:12.704776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:15.546987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.866250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.251081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:22.812955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:25.149607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:27.652121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:30.150602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:32.702137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:35.399827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.656876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.048093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:42.289469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:44.714545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:10.404080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:12.830625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:15.683859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:17.994611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.390791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.050986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:25.370062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:27.790613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:30.331584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:32.845428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:35.568171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.789470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.190010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:42.425326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:44.902015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:10.593838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:13.199226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:15.850683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:18.135933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:20.537877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:23.195268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:25.524267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:27.959707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:30.489281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:32.999367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:35.709297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:37.925100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:40.327856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T13:50:42.575880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-13T13:50:53.415953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
review_scorelength_comment_titlelength_comment_messagepayment_installmentspayment_valuenb_itemssum_pricesum_freight_valueproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmrecencepayment_typecustomer_stateproduct_category_name_englishrecence_score
review_score1.000-0.018-0.223-0.022-0.040-0.108-0.030-0.0890.0160.013-0.013-0.015-0.003-0.009-0.0200.0180.0440.0430.043
length_comment_title-0.0181.0000.3210.0050.0320.0260.0270.0540.0310.006-0.011-0.031-0.003-0.019-0.4200.0170.0130.0340.226
length_comment_message-0.2230.3211.0000.0440.0640.0830.0580.069-0.008-0.0050.0350.0120.0190.0130.0160.0000.0210.0200.014
payment_installments-0.0220.0050.0441.0000.3820.0580.3750.2310.0370.0020.2200.1180.1210.1370.0450.1990.0340.0900.040
payment_value-0.0400.0320.0640.3821.0000.2220.9900.5670.1930.0060.5200.2680.3470.275-0.0140.0080.0150.0990.007
nb_items-0.1080.0260.0830.0580.2221.0000.1780.378-0.036-0.056-0.0040.0080.0040.0010.0030.0080.0000.0270.008
sum_price-0.0300.0270.0580.3750.9900.1781.0000.4690.1970.0110.5070.2560.3390.265-0.0100.0080.0130.0930.007
sum_freight_value-0.0890.0540.0690.2310.5670.3780.4691.0000.101-0.0100.4190.2720.2720.262-0.0440.0000.0300.0540.007
product_description_lenght0.0160.031-0.0080.0370.193-0.0360.1970.1011.0000.1540.100-0.0100.132-0.060-0.0650.0120.0190.2140.044
product_photos_qty0.0130.006-0.0050.0020.006-0.0560.011-0.0100.1541.0000.0150.009-0.067-0.0040.0010.0000.0120.1510.027
product_weight_g-0.013-0.0110.0350.2200.520-0.0040.5070.4190.1000.0151.0000.6220.5360.6240.0530.0110.0120.1930.023
product_length_cm-0.015-0.0310.0120.1180.2680.0080.2560.272-0.0100.0090.6221.0000.2600.6400.0750.0100.0080.2570.046
product_height_cm-0.003-0.0030.0190.1210.3470.0040.3390.2720.132-0.0670.5360.2601.0000.3450.0150.0150.0130.2770.041
product_width_cm-0.009-0.0190.0130.1370.2750.0010.2650.262-0.060-0.0040.6240.6400.3451.0000.0570.0120.0120.2790.040
recence-0.020-0.4200.0160.045-0.0140.003-0.010-0.044-0.0650.0010.0530.0750.0150.0571.0000.0400.0270.0970.882
payment_type0.0180.0170.0000.1990.0080.0080.0080.0000.0120.0000.0110.0100.0150.0120.0401.0000.0300.0360.046
customer_state0.0440.0130.0210.0340.0150.0000.0130.0300.0190.0120.0120.0080.0130.0120.0270.0301.0000.0300.035
product_category_name_english0.0430.0340.0200.0900.0990.0270.0930.0540.2140.1510.1930.2570.2770.2790.0970.0360.0301.0000.128
recence_score0.0430.2260.0140.0400.0070.0080.0070.0070.0440.0270.0230.0460.0410.0400.8820.0460.0350.1281.000

Missing values

2023-02-13T13:50:45.250700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T13:50:45.909242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idorder_statusorder_purchase_timestampreview_scorelength_comment_titlelength_comment_messagepayment_typepayment_installmentspayment_valuenb_itemssum_pricesum_freight_valuecustomer_unique_idcustomer_citycustomer_stateproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishorder_purchase_timerecencerecence_score
09ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:334.00.0170.0credit_card,voucher1.038.711.029.998.727c396fd4830fd04220f754e42b4e5bffsao pauloSP268.04.0500.019.08.013.0housewares10/02/2017385.02
131f31efcb333fcbad2b1371c8cf0fa84delivered2017-09-04 11:26:385.00.0102.0credit_card1.044.111.035.398.727c396fd4830fd04220f754e42b4e5bffsao pauloSP2395.01.0350.019.014.012.0baby09/04/2017385.02
2b0830fb4747a6c6d20dea0b8c802d7efdelivered2018-07-24 20:41:374.016.020.0boleto1.0141.461.0118.7022.76af07308b275d755c9edb36a90c618231barreirasBA178.01.0400.019.013.019.0perfumery07/24/201890.05
341ce2a54c0b03bf3443c3d931a367089delivered2018-08-08 08:38:495.00.00.0credit_card3.0179.121.0159.9019.223a653a41f6f9fc3d2a113cf8398680e8vianopolisGO232.01.0420.024.019.021.0auto08/08/201875.05
4f88197465ea7920adcdbec7375364d82delivered2017-11-18 19:28:065.00.0105.0credit_card1.072.201.045.0027.207c142cf63193a1473d2e66489a9ae977sao goncalo do amaranteRN468.03.0450.030.010.020.0pet_shop11/18/2017338.02
58ab97904e6daea8866dbdbc4fb7aad2cdelivered2018-02-13 21:18:395.00.00.0credit_card1.028.621.019.908.7272632f0f9dd73dfee390c9b22eb56dd6santo andreSP316.04.0250.051.015.015.0stationery02/13/2018251.03
6503740e9ca751ccdda7ba28e9ab8f608delivered2017-07-09 21:57:054.00.00.0credit_card6.0175.261.0147.9027.3680bb27c7c16e8f973207a5086ab329e2congonhinhasPR608.01.07150.065.010.065.0auto07/09/2017470.01
79bdf08b4b3b52b5526ff42d37d47f222delivered2017-05-16 13:10:305.00.00.0credit_card3.075.161.059.9915.17932afa1e708222e5821dac9cd5db4caenilopolisRJ956.01.050.016.016.017.0auto05/16/2017524.01
8f54a9f0e6b351c431402b8461ea51999delivered2017-01-23 18:29:091.00.00.0boleto1.035.951.019.9016.0539382392765b6dc74812866ee5ee92a7faxinalzinhoRS432.02.0300.035.035.015.0furniture_decor01/23/2017637.01
931ad1d1b63eb9962463f764d4e6e0c9ddelivered2017-07-29 11:55:025.00.00.0credit_card,voucher1.0169.761.0149.9919.77299905e3934e9e181bfb2e164dd4b4f8sorocabaSP527.01.09750.042.041.042.0office_furniture07/29/2017450.01
customer_idorder_statusorder_purchase_timestampreview_scorelength_comment_titlelength_comment_messagepayment_typepayment_installmentspayment_valuenb_itemssum_pricesum_freight_valuecustomer_unique_idcustomer_citycustomer_stateproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishorder_purchase_timerecencerecence_score
962018e1ec396e317ff4c82a03ce16a0c3eb3delivered2017-10-27 15:21:005.00.077.0credit_card3.0164.301.0142.5021.801a3b8f1d0782ebedbcf220a96cbc1655maceioAL178.01.0400.019.013.019.0perfumery10/27/2017360.02
96202a2f7428f0cafbc8e59f20e1444b67315delivered2017-12-20 09:52:411.00.086.0credit_card1.071.041.055.9015.14a49e8e11e850592fe685ae3c64b40ecacampo do tenentePR372.02.0300.016.06.012.0musical_instruments12/20/2017306.03
96203da2124f134f5dfbce9d06f29bdb6c308delivered2017-10-04 19:57:375.00.00.0credit_card,voucher2.0106.792.069.0137.78c716cf2b5b86fb24257cffe9e7969df8cuiabaMT180.03.0750.026.015.026.0toys10/04/2017383.02
96204f01a6bfcc730456317e4081fe0c9940edelivered2017-01-27 00:30:035.00.00.0credit_card,voucher5.0389.431.0370.0019.43e03dbdf5e56c96b106d8115ac336f47fdivinopolisMG657.01.0750.038.012.025.0health_beauty01/27/2017633.01
9620547cd45a6ac7b9fb16537df2ccffeb5acdelivered2017-02-23 09:05:125.00.00.0credit_card3.0155.991.0139.9016.09831ce3f1bacbd424fc4e38fbd4d66d29sao pauloSP254.02.02500.049.013.041.0furniture_decor02/23/2017606.01
9620639bd1228ee8140590ac3aca26f2dfe00delivered2017-03-09 09:54:055.00.00.0credit_card3.085.081.072.0013.086359f309b166b0196dbf7ad2ac62bb5asao jose dos camposSP1517.01.01175.022.013.018.0health_beauty03/09/2017592.01
962071fca14ff2861355f6e5f14306ff977a7delivered2018-02-06 12:58:584.00.044.0credit_card3.0195.001.0174.9020.10da62f9e57a76d978d02ab5362c509660praia grandeSP828.04.04950.040.010.040.0baby02/06/2018258.03
962081aa71eb042121263aafbe80c1b562c9cdelivered2017-08-27 14:46:435.00.028.0credit_card5.0271.011.0205.9965.02737520a9aad80b3fbbdad19b66b37b30nova vicosaBA500.02.013300.032.090.022.0home_appliances_208/27/2017421.02
96209b331b74b18dc79bcdf6532d51e1637c1delivered2018-01-08 21:28:272.00.053.0credit_card4.0441.162.0359.9881.185097a5312c8b157bb7be58ae360ef43cjapuibaRJ1893.01.06550.020.020.020.0computers_accessories01/08/2018287.03
96210edb027a75a1449115f6b43211ae02a24delivered2018-03-08 20:57:305.00.00.0debit_card1.086.861.068.5018.3660350aa974b26ff12caad89e55993bd6lapaPR569.01.0150.016.07.015.0health_beauty03/08/2018228.04